Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653599 | Neurocomputing | 2005 | 23 Pages |
Abstract
We present a neural network approach to solve exact and inexact graph isomorphism problems for weighted graphs. In contrast to other neural heuristics or related methods this approach is based on a neural refinement procedure to reduce the search space followed by an energy-minimizing matching process. Experiments on random weighted graphs in the range of 100-5000 vertices and on chemical molecular structures are presented and discussed.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Brijnesh J. Jain, Fritz Wysotzki,